Comprehensive evaluation of privacy policies using the contextual integrity framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Online privacy policies are often lengthy and difficult to understand. This may lead many users to avoid reading them despite increasing concerns about how their personal information is managed. This article presents a structured approach to evaluate the transparency and comprehensiveness of privacy policies using a comprehensive set of evaluation questions within the contextual integrity (CI) framework. We use these questions to identify policies' responses to key privacy concerns. Applying the CI framework, we analyze the clarity and context of these responses, identifying any vagueness and contextual issues that could impede a user's understanding of the privacy policy. Using the CI analysis, we quantify the quality of policies' responses, thereby enabling users to make informed decisions about online services or products. We apply our methodology to two popular messaging apps, Telegram and WhatsApp, using them as case studies to systematically uncover the strengths and weaknesses of their privacy policies. The findings demonstrate that our proposed methodology can effectively identify transparency issues and assess the comprehensiveness of privacy policies. This suggests that our approach could serve as a practical alternative to subjective evaluations typically conducted by privacy experts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it